What is the main difference between Decision Trees and Support Vector Machines? a. Decision Trees are prone to overfitting whereas Support Vector Machines are usually reliable.* b. Support Vector Machines are faster in training whereas Decision Trees can attribute greater importance to each feature. c. Decision Trees are used for classification whereas Support Vector Machines are used for regression. d. Support Vector Machines are more accurate than Decision Trees.

What method is used for support vector machines to prevent overfitting? a. Cross-Validation.* b. Feature Selection c. Bagging d. Boosting

What information does a Decision Tree use to split its nodes? a. Variance.* b. Clustering c. Outliers d. Correlations

What type of bias is associated with Decision Trees? a. Selection bias* b. Multicollinearity bias c. Autocorrelation bias d. Heteroscedasticity bias

Support Vector Machines use what type of kernel to identify the distribution of their data? a. Radial Basis Function* b. Exponential Kernel c. Linear Kernel d. Polynomial Kernel

How are the parameters of a Decision Tree measured? a. Information gain.* b. Regression Coefficients c. Classification Accuracy d. Mean Squared Error

How is training performed with Support Vector Machines? a. Gradient Descent.* b. Boosting c. Bagging d. Cross-Validation

What is the purpose of pruning in Decision Trees? a. To reduce the complexity of the tree and avoid overfitting.* b. To reduce bias c. To increase accuracy d. To increase variance

How does Support Vector Machines use Support Vectors? a. To find a maximum margin hyperplane.* b. To assign weights to based on predictive power c. To reduce bias d. To reduce variance

What is the biggest advantage of using Support Vector Machines? a. Fast training.* b. High accuracy c. Poor results with noisy data d. Has a low computational cost